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Prep plan
Updated weekly · Last refresh Aug 30

Pinterest Data Engineer Interview Questions

The questions to prepare for a Pinterest Data Engineer interview. Questions from real interview reports rank first. Updated weekly.

22questions
~3htotal time
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1
PipelinesStart here. 12 questions · ~96 min
Data Quality and Schema EvolutionMedium

Approach for handling schema changes and data quality checks in a high-volume data lake pipeline.

schema evolutionData ModelingQualityPinterest
Fault Tolerance in Data PipelinesHard

Approach for building fault tolerance into a distributed data pipeline, including retries, idempotency, and recovery controls.

InfrastructureIdempotencyQualityPinterest
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityPinterest
Batch vs Stream Processing Trade-offsMedium

Compare batch and stream processing across latency, complexity, cost, and data quality in a modern analytics pipeline.

InfrastructureStream ProcessingETLPinterest
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2
SQL & Data Manipulation4 questions + 3 drills · ~62 min
Optimizing Slow SQL QueriesMedium

Assesses your approach to diagnosing and improving SQL performance.

Performance TuningPinterest
Partitioning and Sharding at ScaleMedium

Evaluates your experience scaling database performance and manageability.

shardingPinterest
Preparing Data for Machine LearningMedium

Assesses your end-to-end data preparation workflow for ML readiness.

data preparationdata cleaningMachine LearningPinterest
SQL Rolling Average and RankingHard
Practice
Recently asked
Practice drill

Use CTEs and window functions to calculate 7-day transaction averages and rank Loft users within each region.

Window FunctionsRankingRunning TotalsLoftArcadisCgi Nederland
Top Saved Pin Categories QueryMedium
Practice
Practice drill

Use joins, date filtering, aggregation, and ROW_NUMBER to find the three most-saved Pin categories for new Pinterest users.

Date FunctionsGroup ByAggregationsPinterest
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
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3
Behavioral & Leadership6 questions · ~48 min
Explaining Technical Trade-offs ClearlyMedium

Tests whether you can translate complex engineering trade-offs into clear business decisions for non-technical stakeholders.

technical communicationTrade-offsStakeholder ManagementPinterest
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The finish line: interview-readyComplete all 22 questions plus 3 hands-on drills to finish this plan.